Implement SDXLTxt2Img.

sdcli/sd15_txt2img.py

@@ -4,7 +4,7 @@ import modal
 import util
 
 stub = modal.Stub("run-stable-diffusion-cli")
-stub.run_inference = modal.Function.from_name("stable-diffusion-cli", "Txt2Img.run_inference")
+stub.run_inference = modal.Function.from_name("stable-diffusion-cli", "SD15Txt2Img.run_inference")
 
 
 @stub.local_entrypoint()

sdcli/sdxl_txt2img.py

@@ -0,0 +1,51 @@
+import time
+
+import modal
+import util
+
+stub = modal.Stub("run-stable-diffusion-cli")
+stub.run_inference = modal.Function.from_name("stable-diffusion-cli", "SDXLTxt2Img.run_inference")
+
+
+@stub.local_entrypoint()
+def main(
+    prompt: str,
+    height: int = 1024,
+    width: int = 1024,
+    samples: int = 5,
+    seed: int = -1,
+    upscaler: str = "",
+    use_face_enhancer: str = "False",
+    output_format: str = "png",
+):
+    """
+    This function is the entrypoint for the Runway CLI.
+    The function pass the given prompt to StableDiffusion on Modal,
+    gets back a list of images and outputs images to local.
+    """
+    directory = util.make_directory()
+    seed_generated = seed
+    for i in range(samples):
+        if seed == -1:
+            seed_generated = util.generate_seed()
+        start_time = time.time()
+        images = stub.run_inference.remote(
+            prompt=prompt,
+            height=height,
+            width=width,
+            seed=seed_generated,
+            upscaler=upscaler,
+            use_face_enhancer=use_face_enhancer == "True",
+            output_format=output_format,
+        )
+        util.save_images(directory, images, seed_generated, i, output_format)
+        total_time = time.time() - start_time
+        print(f"Sample {i} took {total_time:.3f}s ({(total_time)/len(images):.3f}s / image).")
+
+    prompts: dict[str, int | str] = {
+        "prompt": prompt,
+        "height": height,
+        "width": width,
+        "samples": samples,
+    }
+    util.save_prompts(prompts)